Performance Evaluation of a Signal Processing Algorithm with General-Purpose Computing on a Graphics Processing Unit

Abstract: Graphics Processing Units (GPU) are increasingly being used for general-purpose programming, instead of their traditional graphical tasks. This is because of their raw computational power, which in some cases give them an advantage over the traditionally used Central Processing Unit (CPU). This thesis therefore sets out to identify the performance of a GPU in a correlation algorithm, and what parameters have the greatest effect on GPU performance. The method used for determining performance was quantitative, utilizing a clock library in C++ to measure performance of the algorithm as problem size increased. Initial problem size was set to 28 and increased exponentially to 221. The results show that smaller sample sizes perform better on the serial CPU implementation but that the parallel GPU implementations start outperforming the CPU between problem sizes of 29 and 210. It became apparent that GPU’s benefit from larger problem sizes, mainly because of the memory overhead costs involved with allocating and transferring data. Further, the algorithm that is under evaluation is not suited for a parallelized implementation due to a high amount of branching. Logic can lead to warp divergence, which can drastically lower performance. Keeping logic to a minimum and minimizing the number of memory transfers are vital in order to reach high performance with a GPU.